International Journal of Advanced Mechatronic Systems
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International Journal of Advanced Mechatronic Systems (2 papers in press)
Research on Static Reactive Power Generator Based on Asymmetric Distribution Network by Fuzhuan Wu, Shengjun Wen, Sheng Peng Abstract: Negative or zero sequence components are generated when the voltage is asymmetric or harmonic in distribution networks. Meanwhile, the decoupling process of traditional dq transform is complex. To solve the above problems, firstly, the T/4 delay method (T is the period of grid voltage) is presented to separate positive and negative sequence components, which improves the stability of software phase-locked. Then, Software Phase-Locked Loop (SPLL) is designed to ensure the instantaneity of reactive current check. Besides, a double-loop control scheme combining proportional integral (PI) controller for DC voltage outer loop and proportional resonance (PR) controller for AC current inner loop without decoupling is designed by considering the characteristics of traditional PI and PR without static error regulation. It avoids the complicated decoupling process and improves the real-time performance of the system. Finally, both simulation and experimental results are given to verify the feasibility of design scheme in the Static Var Generator (SVG) system by MATLAB/Simulink and experimental platform based on DSP28335. Keywords: Static reactive power generator; asymmetrical distribution network; PR controller; software phase-locked loop.
Data-based Reinforcement Learning for Lane Keeping with Input Saturation by Rui Luo, Dianwei Qian, Qichao Zhang Abstract: With the development of artificial intelligence, autonomous driving has received extensive attention. As a very complex integrated system, the autonomous vehicle has several modules. This paper is related to the control module, which is used to design an optimal or near-optimal controller to control the desired trajectory of the vehicle. In this paper, lateral control strategy for lane keeping task is proposed based on the model-free reinforcement learning. Different from the model-based methods such as linear quadratic regulator and model predictive control, our method only requires the generated data rather than the perfect knowledge of the system model to guarantee the optimal performance. At the same time, in order to meet two needs of passengers' comfort and fuel economy, input saturation should be considered in the design of the control module. A low-gain state feedback control method is adopted. It mainly solves some algebraic Riccati equations for data-based lateral control. Finally, the corresponding simulation is given and the validity of the algorithm is verified. Keywords: lateral control; lane keeping; input saturation; reinforcement learning.